In many organizations, transfer pricing adjustments are like a lot of other last-minute activities. They seem to be ignored throughout most of the annual cycle. Then, they suddenly take on a great importance at year-end. That leaves the tax team scrambling to address an entire year’s worth of transactions. It also leads to interdepartmental friction in many cases. If transfer pricing is changed retroactively for the entire year, that can have far-reaching implications.

A recent VentureBeat article , “4 AI trends: It’s all about scale in 2022 (so far),” highlighted the importance of scalability. I recommend you read the entire piece, but to me the key takeaway – AI at scale isn’t magic, it’s data – is reminiscent of the 1992 presidential election, when political consultant James Carville succinctly summarized the key to winning – “it’s the economy”.

Self service analytics (also called self-service business intelligence, or self-service BI) is a term commonly used among analytics vendors and organizations adopting BI, often in the context of being the next big thing in driving more people to use data to find insights. But what is self service analytics? How does self service analytics work? And why does self service analytics matter?

dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD).

Unravel now pulls in data quality checks from external tools into its single-pane-of-glass full-stack observability view.

Think about the times you've wished you had more insight into your business data. Or all of the times you wished you could answer questions about your business performance without waiting for someone else to get back to you. Gone are the days when businesses rely solely on IT staff to provide reports and analytics. With self-service business intelligence (BI), users can create their own reports, dashboards, and data visualizations without relying on IT help.

Digital transformation. Everyone has their own ideas about what digital transformation means, so I decided to look up a few definitions.

If you’ve already centralized your log analysis on BigQuery as your single pane of glass for logs & events…congratulations! With the introduction of Log Analytics (Public Preview), something great is now even better. It leverages BigQuery while also reducing your costs and accelerating your time to value with respect to exporting and analyzing your Google Cloud logs in BigQuery.

There’s a lot of talk in the market these days about data clean rooms, along with some confusion about what exactly a data clean room is and how it differs from data sharing methods. In this blog post, I’d like to shed some light on this topic.

DBTA recently hosted a roundtable webinar with four industry experts on “Unlocking the Value of Cloud Data and Analytics.” Moderated by Stephen Faig, Research Director, Unisphere Research and DBTA, the webinar featured presentations from Progress, Ahana, Reltio, and Unravel. You can see the full 1-hour webinar “Unlocking the Value of Cloud Data and Analytics” below. Here’s a quick recap of what each presentation covered.